You're Invited:Meet the Socket Team at BlackHat and DEF CON in Las Vegas, Aug 4-6.RSVP
Socket
Book a DemoInstallSign in
Socket

treesimulator

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

treesimulator

Simulation of rooted phylogenetic trees under a given Multi-Type Birth–Death model (with or without Contact Tracing) (with or without a Skyline).

0.2.18
pipPyPI
Maintainers
1

treesimulator

Simulation of rooted phylogenetic trees under a given Multi-Type Birth–Death (MTBD) model, with or without contact tracing (CT), and with or without Skyline.

Preprint

treesimulator is described in the S1 Appendix of the following article:

Anna Zhukova, Olivier Gascuel. Accounting for contact tracing in epidemiological birth-death models. medRxiv 2024.09.09.24313296; doi:10.1101/2024.09.09.24313296

DOI:10.1101/2024.09.09.24313296 GitHub release PyPI version PyPI downloads Docker pulls

MTBD

The MTBD models were introduced by Stadler & Bonhoeffer [Philos. Trans. R. Soc. B 2013].

An MTBD model with m states has

m(m-1) state transition rate parameters:

  • μij -- transition rate from state i to state j (1 ≤ i, j ≤ m; i ≠ j), where μij ≥ 0 (In practice, we ask the user to provide an m x m matrix for MTBD transition rates. μii must be 0 for all i)

m2 transmission rate parameters:

  • λij -- transmission rate from state i (donor) to state j (recipient) (1 ≤ i, j ≤ m), where λij ≥ 0

m removal (becoming non-infectious) rate parameters:

  • ψi -- removal rate of state i (1 ≤ i ≤ m), where ψi ≥ 0

m sampling probability upon removal parameters:

  • pi -- probability to sample the pathogen of an individual in state i upon removal (1 ≤ i ≤ m), where 0 < pi ≤ 1

Using these probabilities, one can calculate the equilibrium frequencies $πi$ of the model's states, where 0 ≤ πi ≤ 1 and π1 + ... + πm = 1.

The MTBD model has the following epidemiological parameters:

  • Ri = (Σ1≤j≤m λij)/ψi -- reproduction number of state i
  • di = 1/(Σ1≤j≤m;i≠jμij + ψi) -- exit time from state i

Contact Tracing (CT)

Contact tracing extension was introduced by Zhukova & Gascuel [medRxiv 2024]. It adds two parameters to the initial MTBD model:

  • υ -- probability to notify contacts upon sampling
  • φ -- notified contact removal and sampling rate: φ >> ψi ∀i (1 ≤ i ≤ m). The pathogen of a notified contact is sampled automatically (with a probability of 1) upon removal.

and a meta-parameter κ, which defines how many most recent contacts can be notified by each index case. Each is notified independently, with the probability υ.

CT extension adds m notified contact states (1C, ..., mC) to its original MTBD model, where iC is a notified version of the state i. Transition rates for a notified state iC are analogous to those of i (μiCjCij), while transitions from non-notified states to notified ones and vice versa are not allowed (μiCjjiC=0). Transmission rates for a notified state iC are the same as those of i (λiCjij), where the recipients are always in a non-notified state (λiCjCijC=0). The removal rate for a notified state iC is φ.

For CT models exit times from notified contact states iC is calculated as:

  • diC = 1/(Σ1≤j≤m;i≠jμiCjC + φ)

Skyline

Skyline was introduced by Stadler et al. [PNAS 2013] and extended to MTBD by Kühnert et al. [MBE 2016]. It enables piece-wise constant parameter value changes. To use a skyline with k models, one needs to specify k-1 model change times t1, ..., k-1, and k sets of model parameters (see above). At time 0 the simulation starts with model 1, it switches to models 2 at time t1, etc. All the models in the Skyline must have the same states. CT-related parameters can also change at skyline changing times, in that case if some skyline intervals do not have CT, υ=0 and any value for φ must be specified for them. The same κ value is shared among all the skyline -CT models.

We pay particular interest to the classical BD model, the BD Exposed-Infectious (BDEI) model, and BD with super-spreading (BDSS), as they are described in [Voznica et al. 2021], and to their -CT(κ) versions.

BD

1 state:

  • I (infectious)

3 parameters:

  • λ = λI -- transmission rate
  • ψ = ψI -- removal rate
  • p = pI -- sampling probability upon removal

Epidemiological parameters:

  • R = RI = λ/ψ -- reproduction number
  • dI = 1/ψ -- infectious time

BD-CT(κ)

2 states:

  • I, infectious
  • IC, notified infectious contact

5 parameters:

  • λ = λI = λIC -- transmission rate
  • ψ = ψI -- removal rate of I
  • p = pI -- sampling probability upon removal of I
  • υ -- probability to notify contacts upon sampling
  • φ -- notified contact removal and sampling rate of IC: φ >> ψ

BDEI

2 states:

  • E, exposed, i.e. infected but not yet infectious
  • I, infectious

4 parameters:

  • μ = μEI -- transition rate from E to I (becoming infectious)
  • λ = λIE -- transmission rate from I to E
  • ψ = ψI -- removal rate of I
  • p = pI -- sampling probability upon removal of I

BDEI-specific epidemiological parameter:

  • dE = 1/μ -- incubation period

BDEI-CT(κ)

4 states:

  • E, exposed, i.e. infected but not yet infectious
  • I, infectious
  • EC, notified exposed contact
  • IC, notified infectious contact

6 parameters:

  • μ = μEI = μECIC-- transition rate from an exposed state (notified or not) to the corresponding infectious state (becoming infectious)
  • λ = λIE = λICE -- transmission rate from an infectious state (notified or not) to E
  • ψ = ψI -- removal rate of I
  • p = pI -- sampling probability upon removal
  • υ -- probability to notify contacts upon sampling
  • φ -- notified contact removal and sampling rate: φ >> ψ

BDSS

2 states:

  • I, standard infectious individual (a.k.a. normal spreader)
  • S, superspreader

5(+1) parameters:

  • λnn = λII -- transmission rate from I to I

  • λns = λIS -- transmission rate from I to S

  • λsn = λSI -- transmission rate from S to I

  • λss = λSS -- transmission rate from S to S

    (with a constraint that λssnssnnn)

  • ψ = ψI = ψS -- removal rate

  • p = pI = pS -- sampling probability upon removal

BDSS-specific epidemiological parameters:

  • XSssnssnnn -- super-spreading transmission ratio
  • fSss/(λsn + λss) -- super-spreading fraction

BDSS-CT(κ)

4 states:

  • I, standard infectious individual (a.k.a. normal spreader)
  • S, superspreader
  • IC, notified normal spreader
  • SC, notified superspreader

7(+1) parameters:

  • λnn = λII = λICI -- transmission rate from a normal spreader (notified or not) to I

  • λns = λIS = λICS -- transmission rate from a normal spreader (notified or not) to S

  • λsn = λSI = λSCI -- transmission rate from a superspreader (notified or not) to I

  • λss = λSS = λSCS -- transmission rate from a superspreader (notified or not) to S

    (with a constraint that λssnssnnn)

  • ψ = ψI = ψS -- removal rate of a non-notified state

  • p = pI = pS -- sampling probability upon removal of a non-notified state

  • υ -- probability to notify contacts upon sampling

  • φ -- notified contact removal and sampling rate: φ >> ψ

BDEISS

3 states:

  • E, exposed, i.e. infected but not yet infectious
  • I, standard infectious individual (a.k.a. normal spreader)
  • S, infectious superspreader

6 parameters:

  • μn = μEI -- transition rate from E to I (becoming infectious for normal spreaders)
  • μs = μES -- transition rate from E to S (becoming infectious for superspreaders)
  • λn = λIE -- transmission rate from I to E
  • λs = λSE -- transmission rate from S to E
  • ψ = ψI = ψS -- removal rate of I and of S (the same)
  • p = pI = pS -- sampling probability upon removal (the same for I and S)

BDEISS-specific epidemiological parameters:

  • XS = λsn -- super-spreading transmission ratio
  • fS = μs/(μn + μs) -- super-spreading fraction (among infectious individuals)
  • dE = 1/(μn + μs) -- incubation period

BDEISS-CT(κ)

6 states:

  • E, exposed, i.e. infected but not yet infectious
  • N, standard infectious individual (a.k.a. normal spreader)
  • S, superspreader
  • EC, notified exposed individual
  • IC, notified normal spreader
  • SC, notified superspreader

8 parameters:

  • μn = μEI = μECIC -- transition rate from an exposed state (notified or not) to the corresponding normal infectious state (becoming infectious for normal spreaders)
  • μs = μES = μECSC -- transition rate from an exposed state (notified or not) to the corresponding superspreader infectious state (becoming infectious for superspreaders)
  • λn = λIE = λICE -- transmission rate from I or IC to E
  • λs = λSE = λSCE -- transmission rate from S or SC to E
  • ψ = ψI = ψS -- removal rate of I and of S (the same)
  • p = pI = pS -- sampling probability upon removal of I or S
  • υ -- probability to notify contacts upon sampling
  • φ -- notified contact removal and sampling rate: φ >> ψ

Installation

There are 4 alternative ways to run treesimulator on your computer: with docker, apptainer, in Python3, or via command line (requires installation with Python3).

Installation in python3 or command-line

You could either install python (version 3.6 or higher) system-wide and then install treesimulator via pip:

sudo apt install -y python3 python3-pip python3-setuptools python3-distutils
pip3 install treesimulator

or alternatively, you could install python (version 3.6 or higher) and treesimulator via conda (make sure that conda is installed first).

(Optional) to install treesimulator in a new conda environment (e.g., called phyloenv below), first create and activate the environment:

conda create --name phyloenv python=3.6
conda activate phyloenv

Install treesimulator with conda

conda install treesimulator

Basic usage in a command line

If you installed treesimulator in a conda environment (here named phyloenv), do not forget to first activate it, e.g.

conda activate phyloenv

BD, BD-CT(κ) and BD-CT(κ)-Skyline

The following command simulates a tree with 200-500 tips under the BD model, with λ=0.5, ψ=0.25, p=0.5, and saves it to the file tree.nwk, while saving the parameters to the comma-separated file params.csv:

generate_bd --min_tips 200 --max_tips 500 \
--la 0.5 --psi 0.25 --p 0.5 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BD-CT(1) model, with λ=0.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2, and allowing to notify only the most recent contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bd --min_tips 200 --max_tips 500 \
--la 0.5 --psi 0.25 --p 0.5 \
--phi 2.5 --upsilon 0.2 --max_notified_contacts 1 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BD-CT(1)-Skyline model with two time intervals, with λ=0.5, ψ=0.25, p=0.5, φ=2.5, υ=0 between t=0 and t=3, and λ=1, ψ=0.25, p=0.75, φ=2.5, υ=0.2 starting at t=3, and allowing to notify only the most recent contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bd --min_tips 200 --max_tips 500 \
--la 0.5 1 --psi 0.25 0.25 --p 0.5 0.75 \
--phi 2.5 2.5 --upsilon 0 0.2 --max_notified_contacts 1 \
--skyline_times 3 \
--nwk tree.nwk --log params.csv

To see detailed options, run:

generate_bd --help

BDEI, BDEI-CT(κ) and BDEI-CT(κ)-Skyline

The following command simulates a tree with 200-500 tips under the BDEI model, with μ=1, λ=0.5, ψ=0.25, p=0.5, and saves it to the file tree.nwk, while saving the parameters to the comma-separated file params.csv:

generate_bdei --min_tips 200 --max_tips 500 \
--mu 1 --la 0.5 --psi 0.25 --p 0.5 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDEI-CT(2) model, with μ=1, λ=0.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2, and allowing to notify last two contacts of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdei --min_tips 200 --max_tips 500 \
--mu 1 --la 0.5 --psi 0.25 --p 0.5 \
--phi 2.5 --upsilon 0.2 --max_notified_contacts 2 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDEI-CT(2)-Skyline model with three time intervals, with μ=1, λ=0.5, ψ=0.25, p=0.2, φ=2.5, υ=0.2, between t=0 and t=2, with μ=1, λ=0.5, ψ=0.3, p=0.3, φ=2.5, υ=0.3, between t=2 and t=3, and μ=1, λ=0.5, ψ=0.5, p=0.5, φ=5, υ=0.3 starting at t=3, and allowing to notify last two contacts of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdei --min_tips 200 --max_tips 500 \
--mu 1 1 1 --la 0.5 0.5 0.5 --psi 0.25 0.3 0.5 --p 0.2 0.3 0.5 \
--phi 2.5 2.5 5 --upsilon 0.2 0.3 0.3 --max_notified_contacts 2 \
--skyline_times 2 3 \
--nwk tree.nwk --log params.csv

To see detailed options, run:

generate_bdei --help

BDSS, BDSS-CT(κ) and BDSS-CT(κ)-Skyline

The following command simulates a tree with 200-500 tips under the BDSS model, with λnn=0.1, λns=0.3, λsn=0.5, λss=1.5, ψ=0.25, p=0.5, and saves it to the file tree.nwk, while saving the parameters to the comma-separated file params.csv:

generate_bdss --min_tips 200 --max_tips 500 \
--la_nn 0.1 --la_ns 0.3 --la_sn 0.5 --la_ss 1.5 --psi 0.25 --p 0.5 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDSS-CT(3) model, with λnn=0.1, λns=0.3, λsn=0.5, λss=1.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2, and allowing to notify last three contacts of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdss --min_tips 200 --max_tips 500 \
--la_nn 0.1 --la_ns 0.3 --la_sn 0.5 --la_ss 1.5 --psi 0.25 --p 0.5 \
--phi 2.5 --upsilon 0.2 --max_notified_contacts 3 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDSS-CT(3)-Skyline model with two time intervals, with λnn=0.1, λns=0.3, λsn=0.5, λss=1.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2 between t=0 and t=2, and λnn=0.1, λns=0.3, λsn=1, λss=3, ψ=0.25, p=0.5, φ=5, υ=0.5 starting at t=2, and allowing to notify last three contacts of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdss --min_tips 200 --max_tips 500 \
--la_nn 0.1 0.1 --la_ns 0.3 0.3 --la_sn 0.5 1 --la_ss 1.5 3 --psi 0.25 0.25 --p 0.5 0.5 \
--phi 2.5 5 --upsilon 0.2 0.5 --max_notified_contacts 3 \
--skyline_times 2 \
--nwk tree.nwk --log params.csv

To see detailed options, run:

generate_bdss --help

BDEISS, BDEISS-CT(κ) and BDEISS-CT(κ)-Skyline

The following command simulates a tree with 200-500 tips under the BDEISS model, with μn=0.1, μs=0.3, λn=0.5, λs=1.5, ψ=0.25, p=0.5, and saves it to the file tree.nwk, while saving the parameters to the comma-separated file params.csv:

generate_bdeiss --min_tips 200 --max_tips 500 \
--mu_n 0.1 --mu_s 0.3 --la_n 0.5 --la_s 1.5 --psi 0.25 --p 0.5 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDEISS-CT(1) model, with μn=0.1, μs=0.3, λn=0.5, λs=1.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2, and allowing to notify the last contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdeiss --min_tips 200 --max_tips 500 \
--mu_n 0.1 --mu_s 0.3 --la_n 0.5 --la_s 1.5 --psi 0.25 --p 0.5 \
--phi 2.5 --upsilon 0.2 --max_notified_contacts 1 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under the BDSS-CT(1)-Skyline model with two time intervals, with μn=0.1, μs=0.3, λn=0.5, λs=1.5, ψ=0.25, p=0.5, φ=2.5, υ=0.2 between t=0 and t=2, and μn=0.1, μs=0.3, λn=0.5, λs=1.5, ψ=0.25, p=0.2, φ=2.5, υ=0.5 starting at t=2, and allowing to notify the last contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_bdeiss --min_tips 200 --max_tips 500 \
--mu_n 0.1 0.1 --mu_s 0.3 0.3 --la_n 0.5 0.5 --la_s 1.5 1.5 --psi 0.25 0.25 --p 0.5 0.2 \
--phi 2.5 2.5 --upsilon 0.2 0.5 --max_notified_contacts 1 \
--skyline_times 2 \
--nwk tree.nwk --log params.csv

To see detailed options, run:

generate_bdss --help

User-defined MTBD, MTBD-CT(κ) and MTBD-CT(κ)-Skyline models

The following command simulates a tree with 200-500 tips under a generic MTBD model, with two states A and B, with μab=0.6, μba=0.7 (note that μaabb=0 as only transitions between different states are possible!), λaa=0.1, λab=0.2, λba=0.3, λbb=0.4, ψa=0.05, ψb=0.08, p=a0.15, p=b0.65, and saves it to the file tree.nwk, while saving the parameters to the comma-separated file params.csv:

generate_mtbd --min_tips 200 --max_tips 500 \
--states A B \
--transition_rates 0 0.6 0.7 0 \
--transmission_rates 0.1 0.2 0.3 0.4 \
--removal_rates 0.05 0.08 \
--sampling_probabilities 0.15 0.65 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under a generic MTBD-CT(1) model, with two states A and B, with μab=0.6, μba=0.7, λaa=0.1, λab=0.2, λba=0.3, λbb=0.4, ψa=0.05, ψb=0.08, p=a0.15, p=b0.65, φ=2.5, υ=0.2, and allowing to notify only the most recent contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_mtbd --min_tips 200 --max_tips 500 \
--states A B \
--transition_rates 0 0.6 0.7 0 \
--transmission_rates 0.1 0.2 0.3 0.4 \
--removal_rates 0.05 0.08 \
--sampling_probabilities 0.15 0.65 \
--phi 2.5 --upsilon 0.2 --max_notified_contacts 1 \
--nwk tree.nwk --log params.csv

The following command simulates a tree with 200-500 tips under a generic MTBD-CT(1)-Skyline model, with two states A and B, with μab=0.6, μba=0.7, λaa=0.1, λab=0.2, λba=0.3, λbb=0.4, ψa=0.05, ψb=0.08, p=a0.15, p=b0.65, φ=2.5, υ=0.2 between t=0 and t=8, and μab=1.6, μba=1.7, λaa=1.1, λab=1.2, λba=1.3, λbb=1.4, ψa=1.05, ψb=1.08, p=a0.1, p=b0.6, φ=3.5, υ=0.4 starting at t=8, and allowing to notify only the most recent contact of each sampled index case. The simulated tree is saved to the file tree.nwk, while the model parameters are saved to the comma-separated file params.csv:

generate_mtbd --min_tips 200 --max_tips 500 \
--states A B \
--transition_rates 0 0.6 0.7 0 0 1.6 1.7 0 \
--transmission_rates 0.1 0.2 0.3 0.4 1.1 1.2 1.3 1.4 \
--removal_rates 0.05 0.08 1.05 1.08 \
--sampling_probabilities 0.15 0.65 0.1 0.6 \
--phi 2.5 3.5 --upsilon 0.2 0.4 --max_notified_contacts 1 \
--skyline_times 8 \
--nwk tree.nwk --log params.csv

To see detailed options, run:

generate_mtbd --help

Basic usage in Python3

To simulate trees with 200-500 tips under the above models and settings:

from treesimulator.generator import generate
from treesimulator import save_forest
from treesimulator.mtbd_models import Model, BirthDeathModel, BirthDeathExposedInfectiousModel,
  BirthDeathWithSuperSpreadingModel, BirthDeathExposedInfectiousWithSuperSpreadingModel, CTModel

# 1. BD, BD-CT(1) and BD-CT(1)-Skyline
## BD model
bd_model = BirthDeathModel(p=0.5, la=0.5, psi=0.25)
print(bd_model.get_epidemiological_parameters())
[bd_tree], _, _ = generate([bd_model], min_tips=200, max_tips=500)
save_forest([bd_tree], 'BD_tree.nwk')
## Adding -CT to the model above
bdct_model = CTModel(model=bd_model, upsilon=0.2, phi=2.5)
[bdct_tree], _, _ = generate([bdct_model], min_tips=200, max_tips=500, max_notified_contacts=1)
save_forest([bdct_tree], 'BDCT_tree.nwk')
## BD-CT(1)-Skyline models
bdct_model_1 = CTModel(BirthDeathModel(p=0.5, la=0.5, psi=0.25),
                       upsilon=0, phi=2.5)
bdct_model_2 = CTModel(BirthDeathModel(p=0.75, la=1, psi=0.25),
                       upsilon=0.2, phi=2.5)
[bdct_skyline_tree], _, _ = generate([bdct_model_1, bdct_model_2], skyline_times=[3],
                                     min_tips=200, max_tips=500, max_notified_contacts=1)
save_forest([bdct_skyline_tree], 'BDCTSkyline_tree.nwk')

# BDEI, BDEI-CT(2) and BDEI-CT(2)-Skyline
## BDEI model
bdei_model = BirthDeathExposedInfectiousModel(p=0.5, mu=1, la=0.5, psi=0.25)
print(bdei_model.get_epidemiological_parameters())
[bdei_tree], _, _ = generate([bdei_model], min_tips=200, max_tips=500)
save_forest([bdei_tree], 'BDEI_tree.nwk')
## Adding -CT to the model above
bdeict_model = CTModel(model=bdei_model, upsilon=0.2, phi=2.5)
[bdeict_tree], _, _ = generate([bdeict_model], min_tips=200, max_tips=500, max_notified_contacts=2)
save_forest([bdeict_tree], 'BDEICT_tree.nwk')
## BDEI-CT(2)-Skyline with three time intervals
bdeict_model_1 = CTModel(model=BirthDeathExposedInfectiousModel(p=0.2, mu=1, la=0.5, psi=0.25), upsilon=0.2,
                         phi=2.5)
bdeict_model_2 = CTModel(model=BirthDeathExposedInfectiousModel(p=0.3, mu=1, la=0.5, psi=0.3), upsilon=0.3, phi=2.5)
bdeict_model_3 = CTModel(model=BirthDeathExposedInfectiousModel(p=0.5, mu=1, la=0.5, psi=0.5), upsilon=0.3, phi=5)
[bdeict_skyline_tree], _, _ = generate([bdeict_model_1, bdeict_model_2, bdeict_model_3], skyline_times=[2, 3],
                                       min_tips=200, max_tips=500, max_notified_contacts=2)
save_forest([bdeict_skyline_tree], 'BDEICTSkyline_tree.nwk')

# BDSS, BDSS-CT(3) and BDSS-CT(3)-Skyline
## BDSS model
bdss_model = BirthDeathWithSuperSpreadingModel(p=0.5, la_nn=0.1, la_ns=0.3, la_sn=0.5, la_ss=1.5, psi=0.25)
print(bdss_model.get_epidemiological_parameters())
[bdss_tree], _, _ = generate([bdss_model], min_tips=200, max_tips=500)
save_forest([bdss_tree], 'BDSS_tree.nwk')
## Adding -CT to the model above
bdssct_model = CTModel(model=bdss_model, upsilon=0.2, phi=2.5)
[bdssct_tree], _, _ = generate([bdssct_model], min_tips=200, max_tips=500, max_notified_contacts=3)
save_forest([bdssct_tree], 'BDSSCT_tree.nwk')
## BDSS-CT(3)-Skyline with two time intervals, using the model above for the first interval
bdssct_model_2 = CTModel(
  model=BirthDeathWithSuperSpreadingModel(p=0.5, la_nn=0.1, la_ns=0.3, la_sn=1, la_ss=3, psi=0.25),
  upsilon=0.5, phi=5)
[bdssct_skyline_tree], _, _ = generate([bdssct_model, bdssct_model_2], skyline_times=[2], min_tips=200, max_tips=500,
                                       max_notified_contacts=3)
save_forest([bdssct_skyline_tree], 'BDSSCTSkyline_tree.nwk')

# BDEISS, BDEISS-CT(1) and BDEISS-CT(1)-Skyline
## BDEISS model
bdeiss_model = BirthDeathExposedInfectiousWithSuperSpreadingModel(p=0.5, mu_n=0.1, mu_s=0.3, la_n=0.5, la_s=1.5,
                                                                  psi=0.25)
print(bdeiss_model.get_epidemiological_parameters())
[bdeiss_tree], _, _ = generate([bdeiss_model], min_tips=200, max_tips=500)
save_forest([bdeiss_tree], 'BDEISS_tree.nwk')
## Adding -CT to the model above
bdeissct_model = CTModel(model=bdeiss_model, upsilon=0.2, phi=2.5)
[bdeissct_tree], _, _ = generate([bdeissct_model], min_tips=200, max_tips=500, max_notified_contacts=1)
save_forest([bdeissct_tree], 'BDEISSCT_tree.nwk')
## BDEISS-CT(1)-Skyline with two time intervals, using the model above for the first interval
bdeissct_model_2 = CTModel(
  model=BirthDeathExposedInfectiousWithSuperSpreadingModel(p=0.2, mu_n=0.1, mu_s=0.3, la_n=0.5, la_s=1.5, psi=0.25),
  upsilon=0.5, phi=5)
[bdeissct_skyline_tree], _, _ = generate([bdeissct_model, bdeissct_model_2], skyline_times=[2], min_tips=200,
                                         max_tips=500,
                                         max_notified_contacts=1)
save_forest([bdeissct_skyline_tree], 'BDEISSCTSkyline_tree.nwk')

# MTBD, MTBD-CT(1) and MTBD-CT(1)-Skyline
## MTBD model with two states: A and B
mtbd_model = Model(states=['A', 'B'], transition_rates=[[0, 0.6], [0.7, 0]],
                   transmission_rates=[[0.1, 0.2], [0.3, 0.4]],
                   removal_rates=[0.05, 0.08], ps=[0.15, 0.65])
[mtbd_tree], _, _ = generate([mtbd_model], min_tips=200, max_tips=500)
save_forest([mtbd_tree], 'MTBD_tree.nwk')
## Adding -CT to the model above
mtbdct_model = CTModel(model=mtbd_model, upsilon=0.2, phi=2.5)
[mtbdct_tree], _, _ = generate([mtbdct_model], min_tips=200, max_tips=500, max_notified_contacts=1)
save_forest([mtbdct_tree], 'MTBDCT_tree.nwk')
## MTBD-CT(1)-Skyline with two time intervals, using the model above for the first interval
mtbdct_model_2 = CTModel(model=Model(states=['A', 'B'], transition_rates=[[0, 1.6], [1.7, 0]],
                                     transmission_rates=[[1.1, 1.2], [1.3, 1.4]],
                                     removal_rates=[1.05, 1.08], ps=[0.1, 0.6]),
                         upsilon=0.4, phi=3.5)
[mtbdct_skyline_tree], _, _ = generate([mtbdct_model, mtbdct_model_2], skyline_times=[8],
                                       min_tips=200, max_tips=500, max_notified_contacts=1)
save_forest([mtbdct_skyline_tree], 'MTBDCTSkyline_tree.nwk')

Run with apptainer

Once apptainer is installed, run the following command:

apptainer run docker://evolbioinfo/treesimulator

This will launch a terminal session within the container, in which you can run treesimulator following the instructions for the command line ("Basic usage in a command line") above.

Keywords

phylogenetics

FAQs

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts